Project Summary Availability of a validated screening tool for sleep apnea in Veterans afflicted with cardiovascular diseases may provide sufficient lead time to impact the progression of these ailments. This strategy could not be more relevant to high risk groups like those Veterans with established ischemic heart disease. Polysomnography remains the gold standard. Unfortunately, it is expensive, time consuming, and plagued with unyielding flexibility, and limited accessibility. Artificial neural networks have been shown to be superior to physicians'impression or prediction and to equal or exceed traditional statistical models in the prediction of outcomes. A clinical prediction model based on this technology was found to be highly accurate in identifying patients with sleep apnea. We have developed a simple, accurate, and a point-of-care, computer-based clinical decision support system (CDSS) not only to detect the presence of sleep apnea but also to predict its severity. The CDSS is based on deploying an artificial neural network (ANN) derived from anthropomorphic and clinical characteristics. The specific aims of this pilot research project are: 1) to assess the validity of a handheld clinical decision-support system in detecting OSA in patients with ischemic heart disease against polysomnography;and 2) to compare the diagnostic accuracy of the CDSS versus the Berlin questionnaire. To achieve these objectives, we propose a one year study to enroll 123 consecutive Veterans presenting to the cardiology clinics at the VA Western New York Healthcare System. All participants will be asked to complete the Berlin questionnaire. The CDSS will be used to provide an estimate of the AHI. An overnight polysomnography A Bland and Altman plot will be constructed to assess the accuracy of the CDSS versus polysomnography. A receiver operator characteristic curve will be constructed from each of the prediction model and compared for accuracy.